Field of the Invention
This invention relates to fuzzy logic devices which use expert knowledge defined by rules to draw conclusions from input phenomena. More particularly, it relates to apparatus for refining the fuzzy inference rules by: (1) eliminating therefrom the effect of expert knowledge which has little effect on the conclusion, and (2) insuring that knowledge which has greater input on the conclusions is included in the rules.
Fuzzy inference is well known as a method by which the information amount of a phenomenon, which was used to arrive at an inference, can be used to correct or change the conclusion arrived at by inference. (See, for example, Zhang Hongmin, "An Expert System with Thinking in Images," Preprints of the Second IFSA Congress, Tokyo, Jul. 20-25, 1987, p. 765.)
The Fuzzy inference method is used to increase the discriminatory capacity of inference making. For each phenomenon, the information amount of a phenomenon (i.e., the capacity the phenomenon possesses for the discrimination of its information) is determined using a membership function corresponding to each conclusion which may be drawn for that phenomenon. The inference (i.e., the possibility that a conclusion can be drawn) is corrected or changed (by finding the product of the possibility and the information amount) using the information amount of the phenomenon which led to that conclusion. As a result, the discriminability of various inference results can be maximized.
However, in previous fuzzy inference devices, phenomena that could be used to draw a conclusion were lumped together by rules with phenomena which could not be used. Thus, an actual fuzzy inference required voluminous input data, which translated into considerable processing time. This was a serious shortcoming for applications such as diagnostic checks, which require an accurate conclusion in a short time.
Moreover, in existing fuzzy inference schemes the possibility of each conclusion is calculated based on the phenomenon data which have been input, and if not all the data have been input for the phenomena, the possibilities and concomitantly the clearnesses will have very low values. This makes it impossible for a person to evaluate a conclusion accurately.
If all the phenomena data are input, the clearnesses assume high values. However, for applications like diagnostic checks, in which speed is essential, inputting all the data would be too time-consuming.
An experienced specialist can, to an extent, selectively input only the efficient phenomena data. Should an inexperienced novice, however, try inputting an incomplete set of phenomena data, he will not be able to produce high clearness values.